Scene and activity identification in video summary generation based on motion detected in a video

ABSTRACT

Video and corresponding metadata is accessed. Events of interest within the video are identified based on the corresponding metadata, and best scenes are identified based on the identified events of interest. In one example, best scenes are identified based on the motion values associated with frames or portions of a frame of a video. Motion values are determined for each frame and portions of the video including frames with the most motion are identified as best scenes. Best scenes may also be identified based on the motion profile of a video. The motion profile of a video is a measure of global or local motion within frames throughout the video. For example, best scenes are identified from portion of the video including steady global motion. A video summary can be generated including one or more of the identified best scenes.

CROSS REFERENCE To RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/705,864, filed May 6, 2015, which claims priority to, and the benefitof, U.S. Provisional Application No. 62/039,849, filed Aug. 20, 2014,which is incorporated by reference herein in its entirety.

BACKGROUND

1. Technical Field

This disclosure relates to a camera system, and more specifically, toprocessing video data captured using a camera system.

2. Description of the Related Art

Digital cameras are increasingly used to capture videos in a variety ofsettings, for instance outdoors or in a sports environment. However, asusers capture increasingly more and longer videos, video managementbecomes increasingly difficult. Manually searching through raw videos(“scrubbing”) to identify the best scenes is extremely time consuming.Automated video processing to identify the best scenes can be veryresource-intensive, particularly with high-resolution raw-format videodata. Accordingly, an improved method of automatically identifying thebest scenes in captured videos and generating video summaries includingthe identified best scenes can beneficially improve a user's videoediting experience.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The disclosed embodiments have other advantages and features which willbe more readily apparent from the following detailed description of theinvention and the appended claims, when taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a block diagram of a camera system environment according toone embodiment.

FIG. 2 is a block diagram illustrating a camera system, according to oneembodiment.

FIG. 3 is a block diagram of a video server, according to oneembodiment.

FIG. 4 is a flowchart illustrating a method for selecting video portionsto include in a video summary, according to one embodiment.

FIG. 5 is a flowchart illustrating a method for generating videosummaries using video templates, according to one embodiment.

FIG. 6 is a flowchart illustrating a method for generating videosummaries of videos associated with user-tagged events, according to oneembodiment.

FIG. 7 is a flowchart illustrating a method of identifying an activityassociated with a video, according to one embodiment.

FIG. 8 is a flowchart illustrating a method of sharing a video based onan identified activity within the video, according to one embodiment.

FIG. 9 is a flowchart illustrating a method for selecting video portionsto include in a video summary based on motion values associated with thevideo, according to one embodiment.

FIG. 10 is a flowchart illustrating another method for selecting videoportions to include in a video summary based on a motion profile of thevideo, according to one embodiment.

DETAILED DESCRIPTION

The figures and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

Example Camera System Configuration

FIG. 1 is a block diagram of a camera system environment, according toone embodiment. The camera system environment 100 includes one or moremetadata sources 110, a network 120, a camera 130, a client device 135and a video server 140. In alternative configurations, different and/oradditional components may be included in the camera system environment100. Examples of metadata sources 110 include sensors (such asaccelerometers, speedometers, rotation sensors, GPS sensors, altimeters,and the like), camera inputs (such as an image sensor, microphones,buttons, and the like), and data sources (such as external servers, webpages, local memory, and the like). Although not shown in FIG. 1, itshould be noted that in some embodiments, one or more of the metadatasources 110 can be included within the camera 130.

The camera 130 can include a camera body having a camera lens structuredon a front surface of the camera body, various indicators on the frontof the surface of the camera body (such as LEDs, displays, and thelike), various input mechanisms (such as buttons, switches, andtouch-screen mechanisms), and electronics (e.g., imaging electronics,power electronics, metadata sensors, etc.) internal to the camera bodyfor capturing images via the camera lens and/or performing otherfunctions. As described in greater detail in conjunction with FIG. 2below, the camera 130 can include sensors to capture metadata associatedwith video data, such as motion data, speed data, acceleration data,altitude data, GPS data, and the like. A user uses the camera 130 torecord or capture videos in conjunction with associated metadata whichthe user can edit at a later time.

The video server 140 receives and stores videos captured by the camera130 allowing a user to access the videos at a later time. In oneembodiment, the video server 140 provides the user with an interface,such as a web page or native application installed on the client device135, to interact with and/or edit the videos captured by the user. Inone embodiment, the video server 140 generates video summaries ofvarious videos stored at the video server, as described in greaterdetail in conjunction with FIG. 3 and FIG. 4 below. As used herein,“video summary” refers to a generated video including portions of one ormore other videos. A video summary often includes highlights (or “bestscenes”) of a video captured by a user. In some embodiments, best scenesinclude events of interest within the captured video, scenes associatedwith certain metadata (such as an above threshold altitude or speed),scenes associated with certain camera or environment characteristics,and the like. For example, in a video captured during a snowboardingtrip, the best scenes in the video can include jumps performed by theuser or crashes in which the user was involved. In addition to includingone or more highlights of the video, a video summary can also capturethe experience, theme, or story associated with the video withoutrequiring significant manual editing by the user. In one embodiment, thevideo server 140 identifies the best scenes in raw video based on themetadata associated with the video. The video server 140 may thengenerate a video summary using the identified best scenes of the video.The metadata can either be captured by the camera 130 during the captureof the video or can be retrieved from one or more metadata sources 110after the capture of the video.

Metadata includes information about the video itself, the camera used tocapture the video, the environment or setting in which a video iscaptured or any other information associated with the capture of thevideo. For example, metadata can include acceleration datarepresentative of the acceleration of a camera 130 attached to a user asthe user captures a video while snowboarding down a mountain. Suchacceleration metadata helps identify events representing a sudden changein acceleration during the capture of the video, such as a crash theuser may encounter or a jump the user performs. Thus, metadataassociated with captured video can be used to identify best scenes in avideo recorded by a user without relying on image processing techniquesor manual curation by a user.

Examples of metadata include: telemetry data (such as motion data,velocity data, and acceleration data) captured by sensors on the camera130; location information captured by a GPS receiver of the camera 130;compass heading information; altitude information of the camera 130;biometric data such as the heart rate of the user, breathing of theuser, eye movement of the user, body movement of the user, and the like;vehicle data such as the velocity or acceleration of the vehicle, thebrake pressure of the vehicle, or the rotations per minute (RPM) of thevehicle engine; or environment data such as the weather informationassociated with the capture of the video. The video server 140 mayreceive metadata directly from the camera 130 (for instance, inassociation with receiving video from the camera), from a client device135 (such as a mobile phone, computer, or vehicle system associated withthe capture of video), or from external metadata sources 110 such as webpages, blogs, databases, social networking sites, or servers or devicesstoring information associated with the user (e.g., a user may use afitness device recording fitness data).

A user can interact with interfaces provided by the video server 140 viathe client device 135. The client device 135 is any computing devicecapable of receiving user inputs as well as transmitting and/orreceiving data via the network 120. In one embodiment, the client device135 is a conventional computer system, such as a desktop or a laptopcomputer. Alternatively, the client device 135 may be a device havingcomputer functionality, such as a personal digital assistant (PDA), amobile telephone, a smartphone or another suitable device. The user canuse the client device to view and interact with or edit videos stored onthe video server 140. For example, the user can view web pages includingvideo summaries for a set of videos captured by the camera 130 via a webbrowser on the client device 135.

One or more input devices associated with the client device 135 receiveinput from the user. For example, the client device 135 can include atouch-sensitive display, a keyboard, a trackpad, a mouse, a voicerecognition system, and the like. In some embodiments, the client device135 can access video data and/or metadata from the camera 130 or one ormore metadata sources 110, and can transfer the accessed metadata to thevideo server 140. For example, the client device may retrieve videos andmetadata associated with the videos from the camera via a universalserial bus (USB) cable coupling the camera 130 and the client device135. The client device can then upload the retrieved videos and metadatato the video server 140.

In one embodiment, the client device 135 executes an applicationallowing a user of the client device 135 to interact with the videoserver 140. For example, a user can identify metadata properties usingan application executing on the client device 135, and the applicationcan communicate the identified metadata properties selected by a user tothe video server 140 to generate and/or customize a video summary. Asanother example, the client device 135 can execute a web browserconfigured to allow a user to select video summary properties, which intum can communicate the selected video summary properties to the videoserver 140 for use in generating a video summary. In one embodiment, theclient device 135 interacts with the video server 140 through anapplication programming interface (API) running on a native operatingsystem of the client device 135, such as IOS® or ANDROID™. While FIG. 1shows a single client device 135, in various embodiments, any number ofclient devices 135 may communicate with the video server 140.

The video server 140 communicates with the client device 135, themetadata sources 110, and the camera 130 via the network 120, which mayinclude any combination of local area and/or wide area networks, usingboth wired and/or wireless communication systems. In one embodiment, thenetwork 120 uses standard communications technologies and/or protocols.In some embodiments, all or some of the communication links of thenetwork 120 may be encrypted using any suitable technique or techniques.It should be noted that in some embodiments, the video server 140 islocated within the camera 130 itself.

Example Camera Configuration

FIG. 2 is a block diagram illustrating a camera system, according to oneembodiment. The camera 130 includes one or more microcontrollers 202(such as microprocessors) that control the operation and functionalityof the camera 130. A lens and focus controller 206 is configured tocontrol the operation and configuration of the camera lens. A systemmemory 204 is configured to store executable computer instructions that,when executed by the microcontroller 202, perform the camerafunctionalities described herein. A synchronization interface 208 isconfigured to synchronize the camera 130 with other cameras or withother external devices, such as a remote control, a second camera 130, asmartphone, a client device 135, or a video server 140.

A controller hub 230 transmits and receives information from various I/Ocomponents. In one embodiment, the controller hub 230 interfaces withLED lights 236, a display 232, buttons 234, microphones such asmicrophones 222, speakers, and the like.

A sensor controller 220 receives image or video input from an imagesensor 212. The sensor controller 220 receives audio inputs from one ormore microphones, such as microphone 212a and microphone 212b. Metadatasensors 224, such as an accelerometer, a gyroscope, a magnetometer, aglobal positioning system (GPS) sensor, or an altimeter may be coupledto the sensor controller 220. The metadata sensors 224 each collect datameasuring the environment and aspect in which the video is captured. Forexample, the accelerometer 220 collects motion data, comprising velocityand/or acceleration vectors representative of motion of the camera 130,the gyroscope provides orientation data describing the orientation ofthe camera 130, the GPS sensor provides GPS coordinates identifying thelocation of the camera 130, and the altimeter measures the altitude ofthe camera 130. The metadata sensors 224 are rigidly coupled to thecamera 130 such that any motion, orientation or change in locationexperienced by the camera 130 is also experienced by the metadatasensors 224. The sensor controller 220 synchronizes the various types ofdata received from the various sensors connected to the sensorcontroller 220. For example, the sensor controller 220 associates a timestamp representing when the data was captured by each sensor. Thus,using the time stamp, the measurements received from the metadatasensors 224 are correlated with the corresponding video frames capturedby the image sensor 212. In one embodiment, the sensor controller beginscollecting metadata from the metadata sources when the camera 130 beginsrecording a video. In one embodiment, the sensor controller 220 or themicrocontroller 202 performs operations on the received metadata togenerate additional metadata information. For example, themicrocontroller may integrate the received acceleration data todetermine the velocity profile of the camera 130 during the recording ofa video.

Additional components connected to the microcontroller 202 include anI/O port interface 238 and an expansion pack interface 240. The I/O portinterface 238 may facilitate the receiving or transmitting video oraudio information through an I/O port. Examples of I/O ports orinterfaces include USB ports, HDMI ports, Ethernet ports, audioports,and the like. Furthermore, embodiments of the I/O port interface 238 mayinclude wireless ports that can accommodate wireless connections.Examples of wireless ports include Bluetooth, Wireless USB, Near FieldCommunication (NFC), and the like. The expansion pack interface 240 isconfigured to interface with camera add-ons and removable expansionpacks, such as a display module, an extra battery module, a wirelessmodule, and the like.

Example Video Server Architecture

FIG. 3 is a block diagram of an architecture of the video server. Thevideo server 140 in the embodiment of FIG. 3 includes a user storagemodule 305 (“user store” hereinafter), a video storage module 310(“video store” hereinafter), a template storage module 315 (“templatestore” hereinafter), a video editing module 320, a metadata storagemodule 325 (“metadata store” hereinafter), a web server 330, an activityidentifier 335, and an activity storage module 340 (“activity store”hereinafter). In other embodiments, the video server 140 may includeadditional, fewer, or different components for performing thefunctionalities described herein. Conventional components such asnetwork interfaces, security functions, load balancers, failoverservers, management and network operations consoles, and the like arenot shown so as to not obscure the details of the system architecture.

Each user of the video server 140 creates a user account, and useraccount information is stored in the user store 305. A user accountincludes information provided by the user (such as biographicinformation, geographic information, and the like) and may also includeadditional information inferred by the video server 140 (such asinformation associated with a user's previous use of a camera). Examplesof user information include a username, a first and last name, contactinformation, a user's hometown or geographic region, other locationinformation associated with the user, and the like. The user store 305may include data describing interactions between a user and videoscaptured by the user. For example, a user account can include a uniqueidentifier associating videos uploaded by the user with the user's useraccount.

The video store 310 stores videos captured and uploaded by users of thevideo server 140. The video server 140 may access videos captured usingthe camera 130 and store the videos in the video store 310. In oneexample, the video server 140 may provide the user with an interfaceexecuting on the client device 135 that the user may use to uploadvideos to the video store 315. In one embodiment, the video server 140indexes videos retrieved from the camera 130 or the client device 135,and stores information associated with the indexed videos in the videostore. For example, the video server 140 provides the user with aninterface to select one or more index filters used to index videos.Examples of index filters include but are not limited to: the type ofequipment used by the user (e.g., ski equipment, mountain bikeequipment, etc.), the type of activity being performed by the user whilethe video was captured (e.g., snowboarding, mountain biking, etc.), thetime and data at which the video was captured, or the type of camera 130used by the user.

In some embodiments, the video server 140 generates a unique identifierfor each video stored in the video store 310. In some embodiments, thegenerated identifier for a particular video is unique to a particularuser. For example, each user can be associated with a first uniqueidentifier (such as a 10-digit alphanumeric string), and each videocaptured by a user is associated with a second unique identifier made upof the first unique identifier associated with the user concatenatedwith a video identifier (such as an 8-digit alphanumeric string uniqueto the user). Thus, each video identifier is unique among all videosstored at the video store 310, and can be used to identify the user thatcaptured the video.

The metadata store 325 stores metadata associated with videos stored bythe video store 310. For instance, the video server 140 can retrievemetadata from the camera 130, the client device 135, or one or moremetadata sources 110, can associate the metadata with the correspondingvideo (for instance by associating the metadata with the unique videoidentifier), and can store the metadata in the metadata store 325. Themetadata store 325 can store any type of metadata, including but notlimited to the types of metadata described herein. It should be notedthat in some embodiments, metadata corresponding to a video is storedwithin a video file itself, and not in a separate storage module.

The web server 330 provides a communicative interface between the videoserver 140 and other entities of the environment of FIG. 1. For example,the web server 330 can access videos and associated metadata from thecamera 130 or the client device 135 to store in the video store 310 andthe metadata store 325, respectively. The web server 330 can alsoreceive user input provided to the client device 135, can request videosummary templates or other information from a client device 135 for usein generating a video summary, and can provide a generated video summaryto the client device or another external entity.

Event of Interest/Activity Identification

The video editing module 320 analyzes metadata associated with a videoto identify best scenes of the video based on identified events ofinterest or activities, and generates a video summary including one ormore of the identified best scenes of the video. The video editingmodule 320 first accesses one or more videos from the video store 310,and accesses metadata associated with the accessed videos from themetadata store 325. The video editing module 320 then analyzes themetadata to identify events of interest in the metadata. Examples ofevents of interest can include abrupt changes or anomalies in themetadata, such as a peak or valley in metadata maximum or minimum valueswithin the metadata, metadata exceeding or falling below particularthresholds, metadata within a threshold of predetermine values (forinstance, within 20 meters of a particular location or within), and thelike. The video editing module 320 can identify events of interest invideos based on any other type of metadata, such as a heart rate of auser, orientation information, and the like.

For example, the video editing module 320 can identify any of thefollowing as an event of interest within the metadata: a greater thanthreshold change in acceleration or velocity within a pre-determinedperiod of time, a maximum or above-threshold velocity or acceleration, amaximum or local maximum altitude, a maximum or above-threshold heartrate or breathing rate of a user, a maximum or above-threshold audiomagnitude, a user location within a pre-determined threshold distancefrom a pre-determined location, a threshold change in or pre-determinedorientation of the camera or user, a proximity to another user orlocation, a time within a threshold of a pre-determined time, apre-determined environmental condition (such as a particular weatherevent, a particular temperature, a sporting event, a human gathering, orany other suitable event), or any other event associated with particularmetadata.

In some embodiments, a user can manually indicate an event of interestduring capture of the video. For example, a user can press a button onthe camera or a camera remote or otherwise interact with the cameraduring the capture of video to tag the video as including an event ofinterest. The manually tagged event of interest can be indicated withinmetadata associated with the captured video. For example, if a user iscapturing video while snowboarding and presses a camera buttonassociated with manually tagging an event of interest, the cameracreates metadata associated with the captured video indicating that thevideo includes an event of interest, and indicating a time or portionwithin the captured video at which the tagged event of interest occurs.In some embodiments, the manual tagging of an event of interest by auser while capturing video is stored as a flag within a resulting videofile. The location of the flag within the video file corresponds to atime within the video at which the user manually tags the event ofinterest.

In some embodiments, a user can manually indicate an event of interestduring capture of the video using a spoken command or audio signal. Forinstance, a user can say “Tag” or “Tag my moment” during the capture ofvideo to tag the video as including an event of interest. Theaudio-tagged event of interest can be indicated within metadataassociated with the captured video. The spoken command can bepre-programmed, for instance by a manufacturer, programmer, or seller ofthe camera system, or can be customized by a user of the camera system.For instance, a user can speak a command or other audio signal into acamera during a training period (for instance, in response toconfiguring the camera into a training mode, or in response to theselection of a button or interface option associated with training acamera to receive a spoken command). The spoken command or audio signalcan be repeated during the training mode a threshold number of times(such as once, twice, or any number of times necessary for the purposesof identifying audio patterns as described herein), and the camerasystem can identify an audio pattern associated with the spoken commandsor audio signals received during the training period. The audio patternis then stored at the camera, and, during a video capture configuration,the camera can identify the audio pattern in a spoken command or audiosignal received from a user of the camera, and can manually tag an eventof interest during the capture of video in response to detecting thestored audio pattern within the received spoken command or audio signal.In some embodiments, the audio pattern is specific to spoken commands oraudio signals received from a particular user and can be detected onlyin spoken commands or audio signals received from the particular user.In other embodiments, the audio pattern can be identified within spokencommands or audio signals received from any user. It should be notedthat manually identified events of interest can be associated withcaptured video by the camera itself, and can be identified by a systemto which the captured video is uploaded from the camera withoutsignificant additional post-processing.

As noted above, the video editing module 320 can identify events ofinterest based on activities performed by users when the videos arecaptured. For example, a jump while snowboarding or a crash whileskateboarding can be identified as events of interest. Activities can beidentified by the activity identifier module 335 based on metadataassociated with the video captured while performing the activities.Continuing with the previous example, metadata associated with aparticular altitude and a parabolic upward and then downward velocitycan be identified as a “snowboarding jump”, and a sudden slowdown invelocity and accompanying negative acceleration can be identified as a“skateboarding crash”.

The video editing module 320 can identify events of interest based onaudio captured in conjunction with the video. In some embodiments, thevideo editing module identifies events of interest based on one or morespoken words or phrases in captured audio. For example, if audio of auser saying “Holy Smokes!” is captured, the video editing module candetermine that an event of interest just took place (e.g., within theprevious 5 seconds or other threshold of time), and if audio of a usersaying “Oh no! Watch out!” is captured, the video editing module candetermine that an event of interest is about to occur (e.g., within thenext 5 seconds or other threshold of time). In addition to identifyingevents of interest based on captured dialogue, the video editing modulecan identify an event of identify based on captured sound effects,captured audio exceeding a magnitude or pitch threshold, or capturedaudio satisfying any other suitable criteria.

In some embodiments, the video editing module 320 can identify videothat does not include events of interest. For instance, the videoediting module 320 can identify video that is associated with metadatapatterns determined to not be of interest to a user. Such patterns caninclude metadata associated with a below-threshold movement, abelow-threshold luminosity, a lack of faces or other recognizableobjects within the video, audio data that does not include dialogue orother notable sound effects, and the like. In some embodiments, videodetermined to not include events of interest can be disqualified fromconsideration for inclusion in a generated video summary, or can behidden from a user viewing captured video (in order to increase thechance that the remaining video presented to the user does includeevents of interest).

The activity identifier module 335 can receive a manual identificationof an activity within videos from one or more users. In someembodiments, activities can be tagged during the capture of video. Forinstance, if a user is about to capture video while performing asnowboarding jump, the user can manually tag the video being captured orabout to be captured as “snowboarding jump”. In some embodiments,activities can be tagged after the video is captured, for instanceduring playback of the video. For instance, a user can tag an activityin a video as a skateboarding crash upon playback of the video.

Activity tags in videos can be stored within metadata associated withthe videos. For videos stored in the video store 310, the metadataincluding activity tags associated with the videos is stored in themetadata store 325. In some embodiments, the activity identifier module335 identifies metadata patterns associated with particular activitiesand/or activity tags. For instance, metadata associated with severalvideos tagged with the activity “skydiving” can be analyzed to identifysimilarities within the metadata, such as a steep increase inacceleration at a high altitude followed by a high velocity atdecreasing altitudes. Metadata patterns associated with particularactivities are stored in the activity store 340.

In some embodiments, metadata patterns associated with particularactivities can include audio data patterns. For instance, particularsound effects, words or phrases of dialogue, or the like can beassociated with particular activities. For example, the spoken phrase“nice wave” can be associated with surfing, and the sound of a revvingcar engine can be associated with driving or racing a vehicle. In someembodiments, metadata patterns used to identify activities can includethe use of particular camera mounts associated with the activities incapturing video. For example, a camera can detect that it is coupled toa snowboard mount, and video captured while coupled to the snowboardmount can be associated with the activity of snowboarding.

Once metadata patterns associated with particular activities areidentified, the activity identifier module 335 can identify metadatapatterns in metadata associated with other videos, and can tag orassociate other videos associated with metadata including the identifiedmetadata patterns with the activities associated with the identifiedmetadata patterns. The activity identifier module 335 can identify andstore a plurality of metadata patterns associated with a plurality ofactivities within the activity store 340. Metadata patterns stored inthe activity store 340 can be identified within videos captured by oneuser, and can be used by the activity identifier module 335 to identifyactivities within videos captured by the user. Alternatively, metadatapatterns can be identified within videos captured by a first pluralityof users, and can be used by the activity identifier module 335 toidentify activities within videos captured by a second plurality ofusers including at least one user not in the first plurality of users.In some embodiments, the activity identifier module 335 aggregatesmetadata for a plurality of videos associated with an activity andidentifies metadata patterns based on the aggregated metadata. As usedherein, “tagging” a video with an activity refers to the association ofthe video with the activity. Activities tagged in videos can be used asa basis to identify best scenes in videos (as described above), and toselect video clips for inclusion in video summary templates (asdescribed below).

Videos tagged with activities can be automatically uploaded to or sharedwith an external system. For instance, if a user captures video, theactivity identifier module 335 can identify a metadata patternassociated with an activity in metadata of the captured video, inreal-time (as the video is being captured), or after the video iscaptured (for instance, after the video is uploaded to the video server140). The video editing module 320 can select a portion of the capturedvideo based on the identified activity, for instance a threshold amountof time or frames around a video clip or frame associated with theidentified activity. The selected video portion can be uploaded orshared to an external system, for instance via the web server 330. Theuploading or sharing of video portions can be based on one or more usersettings and/or the activity identified. For instance, a user can selectone or more activities in advance of capturing video, and captured videoportions identified as including the selected activities can be uploadedautomatically to an external system, and can be automatically shared viaone or more social media outlets.

Best Scene Identification and Video Summary Generation

The video editing module 320 identifies best scenes associated with theidentified events of interest for inclusion in a video summary. Eachbest scene is a video clip, portion, or scene (“video clips”hereinafter), and can be an entire video or a portion of a video. Forinstance, the video editing module 320 can identify video clipsoccurring within a threshold amount of time of an identified event ofinterest (such as 3 seconds before and after the event of interest),within a threshold number of frames of an identified event of interest(such as 24 frames before and after the event of interest), and thelike. The amount of length of a best scene can be pre-determined, and/orcan be selected by a user.

The amount or length of video clip making up a best scene can vary basedon an activity associated with captured video, based on a type or valueof metadata associated with captured video, based on characteristics ofthe captured video, based on a camera mode used to capture the video, orany other suitable characteristic. For example, if an identified eventof interest is associated with an above-threshold velocity, the videoediting module 320 can identify all or part of the video correspondingto above-threshold velocity metadata as the best scene. In anotherexample, the length of a video clip identified as a best scene can begreater for events of interest associated with maximum altitude valuesthan for events of interest associated with proximity to apre-determined location.

For events of interest manually tagged by a user, the length of a videoclip identified as a best scene can be pre-defined by the user, can bemanually selected by the user upon tagging the event of interest, can belonger than automatically-identified events of interest, can be based ona user-selected tagging or video capture mode, and the like. The amountor length of video clips making up best scenes can vary based on theunderlying activity represented in captured video. For instance, bestscenes associated with events of interest in videos captured whileboating can be longer than best scenes associated with events ofinterest in videos captured while skydiving.

In some embodiments, the video editing module 320 identifies best scenesin a video based on various motion values associated with the videoframes of a video. A motion value associated with a frame of a video isa measure of the motion of a portion of or the entire frame of thevideo. Motion values can include motion vectors, which represent amovement of a frame macroblock from a first frame to a second frame.Similarly, a motion value can represent a motion of an object depictedwithin the video from a location in a first frame to a location in asecond frame (and to subsequent locations in subsequent frames). Motionvalues can include a first value representative of a motion directionfor a video frame portion and a second value representative of motionmagnitude of the motion of the video frame portion. A motion value canalso be representative of the motion within an entire frame, forinstance the sum of all motion vectors associated with portions of theframe. In some embodiments, the video editing module 320 analyzes videoto identify motion values for each frame of the video (for instance, foreach of a plurality of potions of each frame of the video).

The video editing module may identify portions of the video from whichto identify best scenes based on the motion values associated with theframes of the video as is further described in conjunction with FIG. 9below. In addition to identifying best scenes based on the motion valuesassociated with each frame the video editing module 320 may identify amotion profile associated with a video and determine from the motionprofile associated with the video portions of the video from which toidentify best scenes. The motion profile associated with a video is ameasure of global or local motion throughout the video. Portions of thevideo including steady global or local motion can be identified as bestscenes as is further described in conjunction with FIG. 10 below.

The identified video portions make up the best scenes as describedherein. The video editing module 320 generates a video summary bycombining or concatenating some or all of the identified best scenesinto a single video. The video summary thus includes video portions ofevents of interest, beneficially resulting in a playable video includingscenes likely to be of greatest interest to a user. The video editingmodule 320 can receive one or more video summary configurationselections from a user, each specifying one or more properties of thevideo summary (such as a length of a video summary, a number of bestscenes for inclusion in the video summary, and the like), and cangenerate the video summary according to the one or more video summaryconfiguration selections. In some embodiments, the video summary is arenderable or playable video file configured for playback on a viewingdevice (such as a monitor, a computer, a mobile device, a television,and the like). The video summary can be stored in the video store 310,or can be provided by the video server 140 to an external entity forsubsequent playback. Alternatively, the video editing module 320 canserve the video summary from the video server 140 by serving each bestscene directly from a corresponding best scene video file stored in thevideo store 310 without compiling a singular video summary file prior toserving the video summary. It should be noted that the video editingmodule 320 can apply one or more edits, effects, filters, and the liketo one or more best scenes within the video summary, or to the entirevideo summary during the generation of the video summary.

In some embodiments, the video editing module 320 ranks identified bestscenes. For instance, best scenes can be ranked based on activities withwhich they are associated, based on metadata associated with the bestscenes, based on length of the best scenes, based on a user-selectedpreference for characteristics associated with the best scenes, or basedon any other suitable criteria. For example, longer best scenes can beranked higher than shorter best scenes. Likewise, a user can specifythat best scenes associated with above-threshold velocities can beranked higher than best scenes associated with above-threshold heartrates. In another example, best scenes associated with jumps or crashescan be ranked higher than best scenes associated with sitting down orwalking. Generating a video summary can include identifying andincluding the highest ranked best scenes in the video summary.

In some embodiments, the video editing module 320 classifies scenes bygenerating a score associated with each of one or more video classesbased on metadata patterns associated with the scenes. Classes caninclude but are not limited to: content-related classes (“snow videos”,“surfing videos”, etc.), video characteristic classes (“high motionvideos”, “low light videos”, etc.), video quality classes, mode ofcapture classes (based on capture mode, mount used, etc.), sensor dataclasses (“high velocity videos”, “high acceleration videos”, etc.),audio data classes (“human dialogue videos”, “loud videos”, etc.),number of cameras used (“single-camera videos”, “multi-camera videos”,etc.), activity identified within the video, and the like. Scenes can bescored for one or more video classes, the scores can be weighted basedon a pre-determined or user-defined class importance scale, and thescenes can be ranked based on the scores generated for the scenes.

In one example, the video editing module 320 analyzes metadataassociated with accessed videos chronologically to identify an order ofevents of interest presented within the video. For example, the videoediting module 320 can analyze acceleration data to identify an orderedset of video clips associated with acceleration data exceeding aparticular threshold. In some embodiments, the video editing module 320can identify an ordered set of events occurring within a pre-determinedperiod of time. Each event in the identified set of events can beassociated with a best scene; if the identified set of events ischronologically ordered, the video editing module 320 can generate avideo summary by a combining video clips associated with each identifiedevent in the order of the ordered set of events.

In some embodiments, the video editing module 320 can generate a videosummary for a user using only videos associated with (or captured by)the user. To identify such videos, the video editing module 320 canquery the video store 3 I 0 to identify videos associated with the user.In some embodiments, each video captured by all users of the videoserver I 40 includes a unique identifier identifying the user thatcaptured the video and identifying the video (as described above). Insuch embodiments, the video editing module 320 queries the video store310 with an identifier associated with a user to identify videosassociated with the user. For example, if all videos associated withUser A include a unique identifier that starts with the sequence“X1Y2Z3” (an identifier unique to User A), the video editing module 320can query the video store 310 using the identifier “X1Y2Z3” to identifyall videos associated with User A. The video editing module 320 can thenidentify best scenes within such videos associated with a user, and cangenerate a video summary including such best scenes as described herein.

In addition to identifying best scenes, the video editing module 320 canidentify one or more video frames that satisfy a set of pre-determinedcriteria for inclusion in a video summary, or for flagging to a user ascandidates for saving as images/photograph stills. The pre-determinedcriteria can include metadata criteria, including but not limited to:frames with high motion (or blur) in a first portion of a frame and lowmotion (or blur) in another portion of a frame, frames associated withparticular audio data (such as audio data above a particular magnitudethreshold or audio data associated with voices or screaming), framesassociated with above-threshold acceleration data, or frames associatedwith metadata that satisfies any other metadata criteria as describedherein. In some embodiments, users can specify metadata criteria for usein flagging one or more video frames that satisfy pre-determinedcriteria. Similarly, in some embodiments, the video editing module 320can identify metadata patterns or similarities in frames selected by auser to save as images/photograph stills, and can identify subsequentvideo frames that include the identified metadata patterns orsimilarities for flagging as candidates to save as images/photographstills.

Video Summary Templates

In one embodiment, the video editing module 320 retrieves video summarytemplates from the template store 315 to generate a video summary. Thetemplate store 315 includes video summary templates each describing asequence of video slots for including in a video summary. In oneexample, each video summary template may be associated with a type ofactivity performed by the user while capturing video or the equipmentused by the user while capturing video. For example, a video summarytemplate for generating video summaries of a ski tip can differ from thevideo summary template for generating video summaries of a mountainbiking trip.

Each slot in a video summary template is a placeholder to be replaced bya video clip or scene when generating a video summary. Each slot in avideo summary template can be associated with a pre-defined length, andthe slots collectively can vary in length. The slots can be orderedwithin a template such that once the slots are replaced with videoclips, playback of the video summary results in the playback of thevideo clips in the order of the ordered slots replaced by the videoclips. For example, a video summary template may include an introductoryslot, an action slot, and a low-activity slot. When generating the videosummary using such a template, a video clip can be selected to replacethe introductory slot, a video clip of a high-action event can replacethe action slot, and a video clip of a low-action event can replace thelow-activity slot. It should be noted that different video summarytemplates can be used to generate video summaries of different lengthsor different kinds.

In some embodiments, video summary templates include a sequence of slotsassociated with a theme or story. For example, a video summary templatefor a ski trip may include a sequence of slots selected to present theski trip narratively or thematically. In some embodiments, video summarytemplates include a sequence of slots selected based on an activitytype. For example, a video summary template associated with surfing caninclude a sequence of slots selected to highlight the activity ofsurfing.

Each slot in a video summary template can identify characteristics of avideo clip to replace the slot within the video summary template, and avideo clip can be selected to replace the slot based on the identifiedcharacteristics. For example, a slot can identify one or more of thefollowing video clip characteristics: motion data associated with thevideo clip, altitude information associated with the video clip,location information associated with the video clip, weather informationassociated with the clip, or any other suitable video characteristic ormetadata value or values associated with a video clip. In theseembodiments, a video clip having one or more of the characteristicsidentified by a slot can be selected to replace the slot.

In some embodiments, a video clip can be selected based on a lengthassociated with a slot. For instance, if a video slot specifies afour-second length, a four-second (give or take a pre-determined timerange, such as 0.5 seconds) video clip can be selected. In someembodiments, a video clip shorter than the length associated with a slotcan be selected, and the selected video clip can replace the slot,reducing the length of time taken by the slot to be equal to the lengthof the selected video clip. Similarly, a video clip longer than thelength associated with a slot can be selected, and either 1) theselected video clip can replace the slot, expanding the length of timeassociated with the slot to be equal to the length of the selected videoclip, or 2) a portion of the selected video clip equal to the lengthassociated with the slot can be selected and used to replace the slot.In some embodiments, the length of time of a video clip can be increasedor decreased to match the length associated with a slot by adjusting theframe rate of the video clip to slow down or speed up the video clip,respectively. For example, to increase the amount of time taken by avideo clip by 30%, 30% of the frames within the video clip can beduplicated. Likewise, to decrease the amount of time taken by a videoclip by 60%, 60% of the frames within the video clip can be removed.

To generate a video summary using a video summary template, the videoediting module 320 accesses a video summary template from the templatestore 315. The accessed video summary template can be selected by auser, can be automatically selected (for instance, based on an activitytype or based on characteristics of metadata or video for use ingenerating the video summary), or can be selected based on any othersuitable criteria. The video editing module 320 then selects a videoclip for each slot in the video summary template, and inserts theselected video clips into the video summary in the order of the slotswithin the video summary template.

To select a video clip for each slot, the video editing module 320 canidentify a set of candidate video clips for each slot, and can selectfrom the set of candidate video clips (for instance, by selecting thedetermined best video from the set of candidate video clips according tothe principles described above). In some embodiments, selecting a videoclip for a video summary template slot identifying a set of videocharacteristics includes selecting a video clip from a set of candidatevideo clips that include the identified video characteristics. Forexample, if a slot identifies a video characteristic of “velocity over15 mph”, the video editing module 320 can select a video clip associatedwith metadata indicating that the camera or a user of the camera wastraveling at a speed of over 15 miles per hour when the video wascaptured, and can replace the slot within the video summary templatewith the selected video clip.

In some embodiments, video summary template slots are replaced by videoclips identified as best scenes (as described above). For instance, if aset of candidate video clips are identified for each slot in a videosummary template, if one of the candidate video slips identified for aslot is determined to be a best scene, the best scene is selected toreplace the slot. In some embodiments, multiple best scenes areidentified for a particular slot; in such embodiments, one of the bestscenes can be selected for inclusion into the video summary based oncharacteristics of the best scenes, characteristics of the metadataassociated with the best scenes, a ranking of the best scenes, and thelike. It should be noted that in some embodiments, if a best scene orother video clip cannot be identified as an above-threshold match forclip requirements associated with a slot, the slot can be removed fromthe template without replacing the slot with a video clip.

In some embodiments, instead of replacing a video summary template slotwith a video clip, an image or frame can be selected and can replace theslot. In some embodiments, an image or frame can be selected thatsatisfies one or more pre-determined criteria for inclusion in a videosummary as described above. In some embodiments, an image or frame canbe selected based on one or more criteria specified by the video summarytemplate slot. For example, if a slot specifies one or morecharacteristics, an image or frame having one or more of the specifiedcharacteristics can be selected. In some embodiments, the video summarytemplate slot can specify that an image or frame is to be selected toreplace the slot. When an image or frame is selected and used to replacea slot, the image or frame can be displayed for the length of timeassociated with the slot. For instance, if a slot is associated with afour-second period of display time, an image or frame selected and usedto replace the slot can be displayed for the four-second duration.

In some embodiments, when generating a video summary using a videosummary template, the video editing module 320 can present a user with aset of candidate video clips for inclusion into one or more videosummary template slots, for instance using a video summary generationinterface. In such embodiments, the user can presented with apre-determined number of candidate video clips for a particular slot,and, in response to a selection of a candidate scene by the user, thevideo editing module 320 can replace the slot with the selectedcandidate video clip. In some embodiments, the candidate video clipspresented to the user for each video summary template slot are the videoclips identified as best scenes (as described above). Once a user hasselected a video clip for each slot in a video summary template, thevideo editing module 320 generates a video summary using theuser-selected video clips based on the order of slots within the videosummary template.

In one embodiment, the video editing module 320 generates video summarytemplates automatically, and stores the video summary templates in thetemplate store 315. The video summary templates can be generatedmanually by experts in the field of video creation and video editing.The video editing module 320 may provide a user with a user interfaceallowing the user to generate video summary templates. Video summarytemplates can be received from an external source, such as an externaltemplate store. Video summary templates can be generated based on videosummaries manually created by users, or based on an analysis of popularvideos or movies (for instance by including a slot for each scene in avideo).

System Operation

FIG. 4 is a flowchart illustrating a method for selecting video portionsto include in a video summary, according to one embodiment. A request togenerate a video summary is received 410. The request can identify oneor more videos for which a video summary is to be generated. In someembodiments, the request can be received from a user (for instance, viaa video summary generation interface on a computing device), or can bereceived from a non-user entity (such as the video server 140 of FIG.1). In response to the request, video and associated metadata isaccessed 420. The metadata includes data describing characteristics ofthe video, the context or environment in which the video was captured,characteristics of the user or camera that captured the video, or anyother information associated with the capture of the video. As describedabove, examples of such metadata include telemetry data describing theacceleration or velocity of the camera during the capture of the video,location or altitude data describing the location of the camera,environment data at the time of video capture, biometric data of a userat the time of video capture, and the like.

Events of interest within the accessed video are identified 430 based onthe accessed metadata associated with the video. Events of interest canbe identified based on changes in telemetry or location data within themetadata (such as changes in acceleration or velocity data), based onabove-threshold values within the metadata (such as a velocity thresholdor altitude threshold), based on local maximum or minimum values withinthe data (such as a maximum heart rate of a user), based on theproximity between metadata values and other values, or based on anyother suitable criteria. Best scenes are identified 440 based on theidentified events of interest. For instance, for each event of interestidentified within a video, a portion of the video corresponding to theevent of interest (such as a threshold amount of time or a thresholdnumber of frames before and after the time in the video associated withthe event of interest) is identified as a best scene. A video summary isthen generated 450 based on the identified best scenes, for instance byconcatenating some or all of the best scenes into a single video.

FIG. 5 is a flowchart illustrating a method for generating videosummaries using video templates, according to one embodiment. A requestto generate a video summary is received 510. A video summary template isselected 520 in response to receiving the request. The selected videosummary template can be a default template, can be selected by a user,can be selected based on an activity type associated with capturedvideo, and the like. The selected video summary template includes aplurality of slots, each associated with a portion of the video summary.The video slots can specify video or associated metadata criteria (forinstance, a slot can specify a high-acceleration video clip).

A set of candidate video clips is identified 530 for each slot, forinstance based on the criteria specified by each slot, based on videoclips identified as “best scenes” as described above, or based on anyother suitable criteria. For each slot, a candidate video clip isselected 540 from among the set of candidate video clips identified forthe slot. In some embodiments, the candidate video clips in each set ofcandidate video clips are ranked, and the most highly ranked candidatevideo clip is selected. The selected candidate video clips are combined550 to generate a video summary. For instance, the selected candidatevideo clips can be concatenated in the order of the slots of the videosummary template with which the selected candidate video clipscorrespond.

FIG. 6 is a flowchart illustrating a method for generating videosummaries of videos associated with user-tagged events, according to oneembodiment. Video is captured 610 by a user of a camera. During videocapture, an input is received 620 from the user indicating an event ofinterest within the captured video. The input can be received, forinstance, through the selection of a camera button, a camera interface,or the like. An indication of the user-tagged event of interest isstored in metadata associated with the captured video. A video portionassociated with the tagged event of interest is selected 630, and avideo summary including the selected video portion is generated 640. Forinstance, the selected video portion can be a threshold number of videoframes before and after a frame associated with the user-tagged event,and the selected video portion can be included in the generated videosummary with one or more other video portions.

FIG. 7 is a flowchart illustrating a method 700 of identifying anactivity associated with a video, according to one embodiment. A firstvideo and associated metadata is accessed 710. An identification of anactivity associated with the first video is received 720. For instance,a user can identify an activity in the first video duringpost-processing of the first video, or during the capture of the firstvideo. A metadata pattern associated with the identified activity isidentified 730 within the accessed metadata. The metadata pattern caninclude, for example, a defined change in acceleration metadata andaltitude metadata.

A second video and associated metadata is accessed 740. The metadatapattern is identified 750 within the metadata associated with the secondvideo. Continuing with the previous example, the metadata associatedwith the second video is analyzed and the defined change in accelerationmetadata and altitude metadata is identified within the examinedmetadata. In response to identifying the metadata pattern within themetadata associated with the second video, the second video isassociated 750 with the identified activity.

FIG. 8 is a flowchart illustrating a method 800 of sharing a video basedon an identified activity within the video, according to one embodiment.Metadata patterns associated with one or more pre-determined activitiesare stored 810. Video and associated metadata are subsequently captured820, and a stored metadata pattern associated with an activity isidentified 830 within the captured metadata. A portion of the capturedvideo associated with the metadata pattern is selected 840, and isoutputted 850 based on the activity associated with the identifiedmetadata pattern and/or one or more user settings. For instance, a usercan select “snowboarding jump” and “3 seconds before and after” as anactivity and video portion length, respectively. In such an example,when a user captures video, a metadata pattern associated with asnowboarding jump can be identified, and a video portion consisting of 3seconds before and 3 seconds after the video associated with thesnowboarding jump can automatically be uploaded to a social mediaoutlet.

FIG. 9 is a flowchart illustrating a method for selecting video portionsto include in a video summary based on motion values associated with avideo, according to one embodiment. A request to generate a videosummary is received 910. The request can identify one or more videos forwhich a video summary is to be generated. In some embodiments, therequest can be received from a user (for instance, via a video summarygeneration interface on a computing device), or can be received from anon-user entity (such as the video server 140 of FIG. 1). In response tothe request, video associated with the request is accessed 920.

A motion value associated with each frame of the accessed video isidentified 930. In one embodiment, the motion value is determined basedon the summation of the magnitude of all the motion vectors in eachframe. In one example, each frame is divided into a plurality ofportions (e.g., macro blocks) and each portion is associated with afirst motion vector along the horizontal axis and a second motion vectoralong the vertical axis. The motion vectors associated with the portionsof a frame represent the motion of elements within the portions of theframe. In this example a motion value is identified 930 for each frameby summing up the motion vectors associated with each portion of theframe. For instance, a net motion vector for each portion is determinedby summing the squares of the motion vectors along the horizontal andvertical axis of each portion, and the motion value for the frame isdetermined by summing the net motion vectors of the portions of theframe. Thus, in this example, the motion value associated with a framerepresents a measure of the total or collective motion of the objects inthe frame.

A differential motion value is identified 940 for each adjacent pair offrames of the accessed video. As the motion value of a frame representsthe velocity of content of the frame, the differential motion valuebetween adjacent frames represents the acceleration of content betweenframes. In one embodiment, the differential motion value for a pair offrames is identified 940 by determining the difference between themotion value associated with each frame. In one example, a differentialmotion value is associated with a frame by determining the difference inmotion values between a frame and an immediately previous frame.

In some embodiments, a threshold number of differential motion valuesassociated with a sequence of frames of the accessed video areaggregated to identify a single differential motion value for thesequence of frames or an average differential motion value for each pairof frames in the sequence. This helps to reduce possible noise in motionvector values associated with the frames of the accessed video andincreases the likelihood of identifying interesting areas ofacceleration in the accessed video. For example, motion vectors formacroblocks at corresponding locations for each frame in a group ofpictures (or “GOP”) (or, for all frames in the GOP other than theI-frame) can be averaged, producing an array of motion vector averages(one for each macroblock location within a frame) and reducing noisethat may otherwise be present within the motion vectors.

Each frame of the accessed video is scored 950 based on the motion valueand the differential motion value associated with each frame. The motionvalue associated with a frame and the differential motion valueassociated with the frame may be combined in a plurality of differentways to generate a score for the frame. For example, the motion valueand the differential motion value may be summed to generate a score fora frame. The score associated with a frame indicates the amount ofmotion associated with the elements or objects within the frame. Thus,frames with high amounts of motions are associated with higher scores ascompared to frames with lower amounts of motion. In some examples, aftereach frame is scored, the plurality of frames are chronologicallyordered and smoothed, with a Gaussian filter for example, to limit thepossible undesirable effects of “noisy” video frames.

Best scenes are identified 960 based on the scores associated with theframes of the accessed video. For instance, a set of frames associatedwith greater than a threshold value of scores are identified and aportion of the accessed video corresponding to each frame in the set offrames (such as a threshold amount of time or a threshold number offrames before and after the frame in the video) is identified as a bestscene. In another example, a top number of scores of the plurality ofscores (e.g., the top 3 scores) are identified. The corresponding framesassociated with top number of scores are identified and portions of theaccessed video corresponding to each of the identified frames (such as athreshold amount of time or a threshold number of frames before andafter the frame in the video) are identified as best scenes. A videosummary is then generated 970 based on the identified best scenes, forinstance by concatenating some or all of the best scenes into a singlevideo.

In some embodiments, a set of frames associated scores that are lowerthan a threshold value (or a threshold number of frames associated withthe lowest scores) are identified and a portion of the accessed videocorresponding to each frame is identified as a best scene. In someembodiments, sequence of successive frames associated with averagescores that are greater than or lower than a pre-determined thresholdare identified, and scene is identified based on each identifiedsequence of frames. In some embodiments, frames are identified by one ofmotion values associated with the frames and differential motion valuesassociated with the frames, and best scenes are identified based on theidentified frames. In some embodiments, frames are identified based onmotion values associated with parts of frames. For instance, for framesrepresentative of scenes including the sky, the motion values associatedwith the portions of frame including the sky may be lower than themotion values associated with the remainder of the frame. In suchembodiments, frames can be identified that include a below-thresholdmotion value associated with a first frame portion and anabove-threshold motion value associated with a second frame portion, andscenes can be identified based on these identified frames.

FIG. 10 is a flowchart illustrating another method for selecting videoportions to include in a video summary based on a motion profile of avideo, according to one embodiment. A request to generate a videosummary is received 1010. The request can identify one or more videosfor which a video summary is to be generated. In some embodiments, therequest can be received from a user (for instance, via a video summarygeneration interface on a computing device), or can be received from anon-user entity (such as the video server 140 of FIG. 1). In response tothe request, video associated with the request is accessed 1020.

Each video frame of the accessed video is associated with global motionand local motion. The global motion associated with a frame is a measureof the motion of the frame as a whole, while the local motion associatedwith a frame is a measure of the motion of a portion of the frame withrespect to the motion of the other portions of the frame. Thus, a frameassociated with a high local motion value includes a portion of theframe with a high motion value with respect to other portions of theframe. The global motion associated with a frame represents a measure ofthe total motion of all the portions of the frame.

Each video frame is divided 1030 into a number of tiles. The number oftiles each frame is divided into may be based on the resolution of theaccessed video. For example, higher resolution video may correspond to agreater number of tiles each frame of the accessed video is divided 1030into. A motion value associated with each tile of each frame of theaccessed video is identified 1040. In one embodiment, the motion valueis determined based on the summation of the magnitude of all the motionvectors in each tile.

A motion profile of the accessed video is determined 1050. The motionprofile of the accessed video represents the percentage of tiles withineach frame having a motion value that is greater than a first thresholdvalue. In alternative embodiments, the motion profile of the accessedvideo further represents the percentage of tiles within each framehaving a motion value below a second threshold value less than the firstthreshold value. The motion profile of the accessed video may beexamined to identify portions of steady global motion, unsteady globalmotion, and steady local motion in the accessed video. Steady globalmotion refers to motion associated with a frame or set of frames of theaccessed video captured by a substantially motionless camera wherein themotion or activity within the frame or set of frames is substantiallydiscernable to a human viewer or above a motion or activity threshold.Unsteady global motion refers to motion associated with a frame or setof frames of the accessed video captured by a camera in motion whereinthe motion or activity within the frame or set of frames issubstantially undiscernible to a human viewer or below a motion oractivity threshold. Steady local motion refers to local motion within aframe or set of frames of the accessed video captured by a substantiallymotionless camera that is substantially discernable to a human viewer orabove a motion or activity threshold. The motion profile of eachaccessed video can be determined in advance, or in response to receivingthe request to generate a video summary. The motion profile of theaccessed video is a measure of global or local motion throughout theaccessed video.

Portions of the accessed video including steady global or local motioncan be identified to determine best scenes for inclusion in a videosummary for the accessed video.

In one embodiment, the motion profile of the accessed video is generatedby putting together motion profile values associated with each frame ofthe accessed video. A motion profile value for each frame is determinedbased on the number of tiles greater than the threshold value, anddividing the number of tiles greater than the threshold value by thetotal number of tiles within each frame. For example, a frame mayinclude 10 tiles, 5 of which have a motion value greater than thethreshold value. The corresponding motion profile value associated withthe frame is 0.5 or 50%, as half the tiles are greater than thethreshold value. The motion profile for the accessed video can begenerated by compiling the motion profile values of each of the framesof the accessed video. Thus, the motion profile for the accessed videocan include a sequence of values, wherein each value is associated witha frame and represents the percentage of tiles within the frame that aregreater than a first threshold value. In some embodiments, the motionprofile for the accessed video can further include a second sequence ofvalues, each associated with a frame and representing the percentage oftiles within the frame that are less than a second threshold value lessthan the first threshold value.

The motion profile of the accessed video may vary, for instance based onthe selection of the first threshold value (and if relevant, secondthreshold value) and thereby may be examined in a number of ways toidentify 1060 best scenes in the accessed video. In one embodiment, thefirst threshold value is set such that portions of the accessed videoincluding unsteady global motion or fast global motion may beidentified. For example, a high threshold value results in portions ofthe video having motion profile values greater than a predeterminedpercentage (e.g., 50%) as representing portions of the video withundesirable amounts of fast global motion. Frames with motion profilevalues greater than the predetermined percentage have a relatively highglobal motion, and can be classified as unsteady and undesirable,possibly indicative of video captured by a shaky camera. Such frames canbe disqualified from consideration as a best scene for inclusion in avideo summary prior to the identification of best scenes as describedabove.

In one embodiment, the second threshold value is set such that portionsof the accessed video including slow moving motion, such as steadyglobal or local motion, may be identified. For example, a relatively lowsecond threshold value results in portions of the video having motionprofile values lower than a certain percentage (e.g., 50%-70%) asrepresenting portions of the video with slow moving motion or steadyglobal and local motion. Frames with motion profile values lower thanthe predetermined percentage have a relatively low global and localmotion, and can be classified as steady and desirable, possiblyindicative of video captured by a steady camera. Best scenes can beidentified based on such frames for inclusion in a video summary, asdescribed above.

In one embodiment, the first and/or second threshold values are set suchthat portions of the accessed video including bands of global motion inthe motion profile of the accessed video may be identified. A band ofglobal motion refers to portions of the motion profile of the accessedvideo including the same or similar motion profile values, such as afirst set of frames of the accessed video having a similar motionprofile value as a second set of frames. For example, a nominalthreshold value results in the motion profile of the accessed videohaving a plurality of motion bands and a plurality of motion peaks. Themotion peaks represent the rapid increases in motion profile values inshort periods of time. Given the nominal threshold the motion profile islikely to have motion bands having values greater than a certainpercentage as well as motion peaks that rise rapidly above the certainpercentage. Motion peaks typically represent rapidly moving undesirableglobal motion as the percentage of tiles having large motion valuesrapidly increases between frames in a short period of time—acharacteristic that may be attributed to unsteady shots. In someembodiments, motion peaks are scored and weighted based on a width ofthe motion peak (determined, for instance, to be the full width of thepeak at half the maximum amplitude of the peak). In such embodiments,peaks with broader widths (which can represent highlights withconsistent motion velocities) may be scored higher than peaks withnarrow widths (which can represent short/jerk camera or object motion).Accordingly, best scenes can be selected based on the scored peaks (forinstance, scenes associated with above-threshold peak scores can beselected).

Motion bands greater than a certain percentage may represent interestingportions of fast moving steady global motion as a similar percentage oftiles in a set of frames have motion values greater than the nominalthreshold value. However, in some examples, motion bands that run forgreater than a threshold period of time or include greater than athreshold number of frames with motion values greater than the nominalthreshold may represent portions of unsteady global motion that may beundesirable to the user. In this example, the portions of the videoincluding motion bands that last or run for less than a threshold periodof time or for less than a threshold number of frames may be identified1060 as best scenes. For example, the portions of the accessed videoincluding motion bands may be scored based on one or more of: the motionvalue associated with each frame, the differential motion valueassociated with each frame, the length of time of the motion band,whether the motion band exceeds a threshold period of time correspondingto unsteady or undesirable video, and/or the number of frames within themotion band. Accordingly, best scenes may be identified based on thescores, as is further described above.

In one embodiment, the first and/or second threshold values are set suchthat portions of the accessed video including high amounts of localmotion or fast local motion may be identified. For example, a nominalthreshold value can be selected such that the motion profile of theaccessed video including motion peaks having maximum motion values lowerthan a certain percentage (e.g., 30%) or within a low range ofpercentages (e.g., 10%-30%) can be determined. As motion peaks representportions of the motion profile of the accessed video within which themotion values rise at a rapid pace or within a few number of frames,motion peaks with relatively low motion profile values representportions of the video including large amounts of local motion, as thepercentage of tiles with motion values higher than the threshold valuewithin a frame is relatively low. However, the remaining tiles withinthe frame and the frames near the frame in question have motion valuesless than the threshold value, therefore implying that the motion peaksare associated with portions of the accessed video including fast localmotion. In this example, the portions of the video including fast localmotion or motion peaks having maximum motion values lower than apercentage may be identified 1060 as best scenes. For example, theportions of the accessed video including fast local motion may be scoredbased on one or more of the motion value associated with each frame, thedifferential motion value associated with each frame, the amount of fastlocal motion within each portion, and the number of motion peaks havingmaximum values lower than a predetermined threshold. Best scenes maythen be identified based on the scores, as described above.

A video summary is then generated 1070 based on the identified bestscenes, for instance by concatenating some or all of the best scenesinto a single video, as described above.

Additional Configuration Considerations

Throughout this specification, some embodiments have used the expression“coupled” along with its derivatives. The term “coupled” as used hereinis not necessarily limited to two or more elements being in directphysical or electrical contact. Rather, the term “coupled” may alsoencompass two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other, or arestructured to provide a thermal conduction path between the elements.

Likewise, as used herein, the terms “comprises,” “comprising,”“includes,” “including,” “has,” “having” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Finally, as used herein any reference to “one embodiment” or “anembodiment” means that a particular element, feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. The appearances of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for acamera expansion module as disclosed from the principles herein. Thus,while particular embodiments and applications have been illustrated anddescribed, it is to be understood that the disclosed embodiments are notlimited to the precise construction and components disclosed herein.Various modifications, changes and variations, which will be apparent tothose skilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

What is claimed is:
 1. A system configured to generate a video summary from recorded video footage, the system comprising: storage media that stores video including multiple frames and audio, the video having an unedited viewing time; one or more physical processor configured by machine readable instructions to: obtain a user-selected summary length for a video summary of the video; obtain metadata for the video, the metadata including biometric information of a user captured contemporaneously with capture of the video, motion values that characterize motion of objects visible within the frames of the video, and camera motion information characterizing position and/or motion of the camera during capture of the video; analyze the biometric information of the user captured contemporaneously with capture of the video, the motion values that characterize motion of objects visible within the frames of the video, the camera motion information characterizing position and/or motion of the camera during capture of the video, and audio captured contemporaneously with the frames of the video to identify events of interest within the video; select portions of the video for inclusion in the video summary based on the identified events of interest and the user-selected summary length; and generate an electronic file defining the video summary from the selected portions of the video so that the video summary has the user-selected summary length.
 2. The system of claim 1, wherein the one or more physical processors are further configured to rank the events of interest within the video based on the biometric information of the user captured contemporaneously with capture of the individual events of interest, the motion values that characterize motion of objects visible within the frames of the individual events of interest, the camera motion information characterizing position and/or motion of the camera during capture of the individual events of interest, and audio captured contemporaneously with capture of the individual events of interest.
 3. The system of claim 2, wherein the one or more physical processors are further configured such that selection of the portions of the video for inclusion in the video summary is based on the biometric information of the user captured contemporaneously with capture of the video, the motion values that characterize motion of objects visible within the frames of the video, the camera motion information characterizing position and/or motion of the camera during capture of the video, and audio captured contemporaneously with capture of the video to identify events of interest within the video includes selection of the portions of the video for inclusion in the video summary based on rankings of the individual events of interest.
 4. The system of claim 1, wherein the events of interest include a first event of interest identified on the basis of the biometric information of the user captured contemporaneously with capture of the first event of interest.
 5. The system of claim 4, wherein the one or more physical processors are configured such that identification of the first event of interest is based on one or more of the biometric information of the user captured contemporaneously with capture of the first event of interest crossing a first threshold, a maximum and/or a minimum in the biometric information of the user captured contemporaneously with capture of the first event of interest, and/or a change in the biometric information of the user captured contemporaneously with the first event of interest.
 6. The system of claim 1, wherein the events of interest include a second event of interest identified on the basis of motion of an object visible within the frames of the second event of interest as characterized by a motion value for the object.
 7. The system of claim 6, wherein the one or more physical processors are configured such that identification of the second event of interest is based on one or more of the motion value that characterizes motion of the object visible within the frames of the second event of interest crossing a second threshold, a maximum and/or a minimum of the motion value that characterizes motion of the object visible within the frames of the second event of interest, and/or a change in the motion value that characterizes motion of the object visible within the frames of the second event of interest.
 8. The system of claim 1, wherein the events of interest include a third event of interest identified on the basis of a change in the position and/or motion of the camera during capture of the video during capture of the third event of interest as characterized by the camera motion information.
 9. The system of claim 8, wherein the one or more physical processors are configured such that identification of the third event of interest is based on the camera motion information for the third event of interest indicating a change in the position and/or motion of the camera.
 10. The system of claim 1, wherein the events of interest include a fourth event of interest identified on the basis of the audio captured contemporaneously with the fourth event of interest.
 11. The system of claim 10 wherein one or more physical processors are configured such that identification of the fourth event of interest is based on one or more of a level change of the audio captured contemporaneously with the fourth event of interest, recognition of a word or phrase in the audio captured contemporaneously with the fourth event of interest, and/or a non-verbal pattern or noise present in the audio captured contemporaneously with the fourth event.
 12. The system of claim 1, wherein the one or more physical processors are configured such that an amount of separate portions of the video selected for inclusion in the video summary is based on the user-selected summary length.
 13. The system of claim 5, wherein the events of interest include a second event of interest identified on the basis of motion of an object visible within the frames of the second event of interest as characterized by a motion value for the object.
 14. The system of claim 13, wherein the one or more physical processors are configured such that identification of the second event of interest is based on one or more of the motion value that characterizes motion of the object visible within the frames of the second event of interest crossing a second threshold, a maximum and/or a minimum of the motion value that characterizes motion of the object visible within the frames of the second event of interest, and/or a change in the motion value that characterizes motion of the object visible within the frames of the second event of interest.
 15. The system of claim 14, wherein the events of interest include a third event of interest identified on the basis of a change in the position and/or motion of the camera during capture of the video during capture of the third event of interest as characterized by the camera motion information.
 16. The system of claim 15, wherein the one or more physical processors are configured such that identification of the third event of interest is based on the camera motion information for the third event of interest indicating a change in the position and/or motion of the camera.
 17. The system of claim 1, wherein the one or more motion values that characterize motion of objects visible within the frames of the video include a differential motion parameter associated with a frame of the frames of the video.
 18. The system of claim 17, wherein: the differential motion parameter is being determined based on a difference of the one or more motion values associated with the frame and the one or more motion values associated with another frame preceding the frame.
 19. The system of claim 17, wherein: the one or more motion values include a first motion value associated with a portion of the frame at a first location within the frame; the one or more motion values include a second motion value associated with another portion of another frame at a second location within another frame, the second location within another frame corresponding to the first location within the frame; and the differential motion parameter is being determined based on a difference operation between the first motion value and the second motion value.
 20. The system of claim 7, wherein the one or more motion values that characterize motion of objects visible within the frames of the video include a differential motion parameter associated with a frame of the frames of the video; the differential motion parameter is being determined based on a difference of the one or more motion values associated with the frame and the one or more motion values associated with another frame preceding the frame. 